Association of hemoglobin with plasma neurofilament light and white matter hyperintensities in Alzheimer's disease continuum
Bibliographic record
Abstract
Objective: This study aimed to investigate the association of hemoglobin (Hb) with axonal injury marker plasma neurofilament light (PNFL) and brain structure measurements in the Alzheimer's disease (AD) continuum. Methods: The data used in this study were collected from the Alzheimer's Disease Neuroimaging Initiative database. Participants with cognitively normal, mild cognitive impairment, and mild dementia were included in the data analyses. All participants had available data on blood tests, PNFL levels, neuropsychological assessments, brain structure measurements (including volumes of white matter hyperintensities [WMH], hippocampus, gray matter, and total brain), and Aβ positron emission tomography standardized uptake value ratio (SUVR) at baseline. Aβ-positive was defined as SUVR threshold value > 1.11. Linear regression, restricted cubic spline, and causal mediation analyses were conducted to investigate the association of Hb concentration with PNFL levels and brain structure measurements. Stratified analyses were also employed to evaluate the association between Hb concentration and PNFL levels across different APOE genotypes and sex. Results: In the Aβ-positive group, Hb concentration was associated with PNFL levels (β = -0.022, p = 0.002). Stratified analyses suggested an association between Hb concentration and PNFL in APOE ɛ4 carriers (β = -0.031, p < 0.001) and males (β = -0.030, p < 0.001) but not in non-carriers and females (p > 0.05). Hb concentration was also associated with WMH volume (β = -0.04, p = 0.028), especially in APOE ɛ4 carriers, with mediation analysis revealing that PNFL mediated the association between Hb concentration and WMH volume. The association of Hb concentration with other brain structure measurements was minimal. Conclusion: In the AD continuum, Hb was associated with axonal injury marker PNFL and WMH volume, particularly in APOE ɛ4 carriers.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".